6th Summer Institute in Statistics for Big Data (SISBID)

This module is currently full. Registrations are closed at this time.

Module 4: Visualization of Biomedical Big Data

Wed, July 22 to Fri, July 24

Module dates/times: Wednesday, July 22; Thursday, July 23, and Friday, July 24. Live sessions will start no earlier than 8 a.m. Pacific and end no later than 2:30 p.m. Pacific, except for Wednesdays. For modules that end on Wednesday, live sessions will end by 11 a.m. Pacific. For modules that start on Wednesday, live sessions will begin no earlier than 11:30 a.m.

We will present general-purpose techniques for visualizing any sort of large data sets, as well as specific techniques for visualizing common types of biological data sets. Often the challenge of visualizing Big Data is to aggregate it down to a suitable level. Understanding Big Data involves an iterative cycle of visualization and modeling. We will illustrate this with several case studies during the workshop.

The first segment of this module will focus on structured development of graphics using static graphics. This will use the ggplot2 package in R. It enables building plots using grammatically defined elements, and producing templates for use with multiple data sets. We will show how to extend these principles for genomic data using the ggplot2-based ggbio package.

The second segment will focus on interactive graphics for rapid exploration of Big Data. We will also demonstrate interactive techniques for high-performance local display using cranvas, and for easily creating interactive web graphics with ggvis. In addition, we will explain how to create simple web GUIs for managing complex summaries of biological data using the shiny package.

We will use a hands-on teaching methodology that combines short lectures with longer practice sessions. As students learn about new techniques, they will also be able to put them into practice and receive feedback from experts. We will teach using R and Rstudio.

Module assumes some familiarity with R (see previous year’s materials as reference).

Recommended Reading: Cookbook for R, by Winston Chang, available at www.cookbook-r.com.